手法を比較

	最小二乗法 (OLS) 回帰	状態空間モデル（カルマンフィルタ）
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	2019	1990
提唱者	Wooldridge (textbook treatment); classical least squares	Harvey; Durbin & Koopman (state space treatment); Kalman filter
種類	Linear regression	State space time series model
原典	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
別名	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
関連	5	4
概要	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.